| Popis: |
Research highlights? Decision-making is a difficult and quite responsible task for doctors. Some of the computer decision models assisted the doctor with some computer decision models. In this study, Takagi-Sugeno type neuro-fuzzy network has been designed to determine anemia level of a child. First the input values have been fuzzified. The triangular membership function has been used for fuzzification. Algebraic product has been used as Tnorm in rule layer. The performance analyses have been obtained by leaving- one-out cross-validation. After statistical measurements, it was found that MPE=_0.0018, MAE=0.2090, MAPE=0.0511, RMSE=0.2743 and R2=0.9957 of this developed system. According to these results, the designed neuro-fuzzy network may be considered as adequate close to traditional decision-making methods and thus the designed network can be used effectively for child anemia prediction. Decision-making is a difficult and quite responsible task for doctors. Some of the computer decision models assisted the doctor with some computer decision models. In this study, neuro-fuzzy network has been designed to determine anemia level of a child. The performance analyses have been obtained by leaving-one-out cross-validation. After statistical measurements, it was found that MPE=-0.0018, MAE=0.2090, MAPE=0.0511, RMSE=0.2743 and R2=0.9957 of this developed system. According to these results, the designed neuro-fuzzy network may be considered as adequate close to traditional decision-making methods and thus the designed network can be used effectively for child anemia prediction. |
Full Text from ScienceDirect